A new dataset of river flood hazard maps for Europe and the Mediterranean Basin region

Abstract. Continental scale hazard maps for riverine floods have grown in importance in the last years. Nowadays, they are used for a variety of research and commercial activities, such as evaluating present and future risk scenarios and adaptation strategies, as well as a support of national and local flood risk management plans. Here, we present a new set of hazard maps for river flooding that covers most of the geographical Europe and all the river basins entering the Mediterranean and Black Seas in the Caucasus, Middle East and Northern Africa countries. Maps represent inundation along 329’000 km of river network at 100 m resolution, for six different flood return periods. The input river flow data is produced by the hydrological model LISFLOOD, while inundation simulations are performed with the 2D hydrodynamic modelling LISFLOOD-FP. To provide an overview of the skill of the new maps, we undertake a detailed validation exercise of the new maps using official hazard maps for Hungary, Italy, Norway, Spain and the United Kingdom. We find that modelled maps can identify on average two-thirds of reference flood extent, however they also overestimate flood-prone areas for flood probabilities below 1-in-100-year, while for return periods equal or above 500 years the maps can correctly identify more than half of flooded areas. We attribute the observed skill to a number of shortcomings of the modelling framework, such as the absence of flood protections and rivers with upstream area below 500 km2, and the limitations in representing river channels and topography of low land areas. In addition, the large variability of reference maps affects the correct identification of the areas for the validation, thus penalizing scores. However, modelled maps achieve comparable results to existing large-scale flood models when using similar parameters for the validation. We conclude that recently released high-resolution elevation datasets combined with reliable data of river channel geometry may greatly contribute to improve future versions of continental-scale flood hazard maps. The database is available for download at https://data.jrc.ec.europa.eu/dataset/1d128b6c-a4ee-4858-9e34-6210707f3c81 (Dottori et al., 2020a).

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Generating Flood Hazard Maps Based on an Innovative Spatial Interpolation Methodology for Precipitation

Atmosphere ◽

10.3390/atmos12101336 ◽

2021 ◽

Vol 12 (10) ◽

pp. 1336

Author(s):

Mohammad Zare ◽

Guy J.-P. Schumann ◽

Felix Norman Teferle ◽

Ruja Mansorian

Keyword(s):

Time Scale ◽

Spatial Interpolation ◽

Interpolation Method ◽

Flood Hazard ◽

Daily Rainfall ◽

Return Periods ◽

Hazard Maps ◽

Rain Gauges ◽

Fcm Clustering ◽

Flood Hazard Maps

In this study, a new approach for rainfall spatial interpolation in the Luxembourgian case study is introduced. The method used here is based on a Fuzzy C-Means (FCM) clustering method. In a typical FCM procedure, there are a lot of available data and each data point belongs to a cluster, with a membership degree [0 1]. On the other hand, in our methodology, the center of clusters is determined first and then random data are generated around cluster centers. Therefore, this approach is called inverse FCM (i-FCM). In order to calibrate and validate the new spatial interpolation method, seven rain gauges in Luxembourg, Germany and France (three for calibration and four for validation) with more than 10 years of measured data were used and consequently, the rainfall for ungauged locations was estimated. The results show that the i-FCM method can be applied with acceptable accuracy in validation rain gauges with values for R2 and RMSE of (0.94–0.98) and (9–14 mm), respectively, on a monthly time scale and (0.86–0.89) and (1.67–2 mm) on a daily time scale. In the following, the maximum daily rainfall return periods (10, 25, 50 and 100 years) were calculated using a two-parameter Weibull distribution. Finally, the LISFLOOD FP flood model was used to generate flood hazard maps in Dudelange, Luxembourg with the aim to demonstrate a practical application of the estimated local rainfall return periods in an urban area.

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Deriving global flood hazard maps of fluvial floods through a physical model cascade

Hydrology and Earth System Sciences ◽

10.5194/hess-16-4143-2012 ◽

2012 ◽

Vol 16 (11) ◽

pp. 4143-4156 ◽

Cited By ~ 120

Author(s):

F. Pappenberger ◽

E. Dutra ◽

F. Wetterhall ◽

H. L. Cloke

Keyword(s):

Land Surface ◽

Large Scale ◽

Flood Hazard ◽

Regional Scale ◽

Surface Model ◽

Return Periods ◽

Hazard Maps ◽

Data Set ◽

Flood Hazard Maps ◽

River Routing

Abstract. Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (re)insurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF) land surface model is coupled to ERA-Interim reanalysis meteorological forcing data, and resultant runoff is passed to a river routing algorithm which simulates floodplains and flood flow across the global land area. The global hazard map is based on a 30 yr (1979–2010) simulation period. A Gumbel distribution is fitted to the annual maxima flows to derive a number of flood return periods. The return periods are calculated initially for a 25 × 25 km grid, which is then reprojected onto a 1 × 1 km grid to derive maps of higher resolution and estimate flooded fractional area for the individual 25 × 25 km cells. Several global and regional maps of flood return periods ranging from 2 to 500 yr are presented. The results compare reasonably to a benchmark data set of global flood hazard. The developed methodology can be applied to other datasets on a global or regional scale.

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An integrated hydrological and hydraulic modelling approach for the flood risk assessment over Po river basin

10.5194/nhess-2019-356 ◽

2019 ◽

Cited By ~ 2

Author(s):

Rita Nogherotto ◽

Adriano Fantini ◽

Francesca Raffaele ◽

Fabio Di Sante ◽

Francesco Dottori ◽

...

Keyword(s):

Ad Hoc ◽

Flood Hazard ◽

Hydraulic Modeling ◽

Return Periods ◽

Hazard Maps ◽

Joint Research ◽

Po River ◽

Essential Information ◽

Flood Prone Areas ◽

Flood Hazard Maps

Abstract. Identification of flood prone areas is instrumental for a large number of applications, ranging from engineering to climate change studies, and provides essential information for planning effective emergency responses. In this work we describe an integrated hydrological and hydraulic modeling approach for the assessment of flood-prone areas in Italy and we present the first results obtained over the Po river (Northern Italy) at a resolution of 90 m. River discharges are obtained through the hydrological model CHyM driven by GRIPHO, a newly-developed high resolution hourly precipitation dataset. Runoff data is then used to obtain Synthetic Design Hydrographs (SDHs) for different return periods along the river network. Flood hydrographs are subsequently processed by a parallelized version of the CA2D hydraulic model to calculate the flow over an ad hoc re-shaped HydroSHEDS digital elevation model which includes information about the channel geometry. Modeled hydrographs and SDHs are compared with those obtained from observed data for a choice of gauging stations, showing an overall good performance of the CHyM model. The flood hazard maps for return periods of 50, 100, 500 are validated by comparison with the official flood hazard maps produced by the River Po Authority (Adbpo) and with the Joint Research Centre's (JRC) pan-European maps. The results show a good agreement with the available official national flood maps for high return periods. For lower return periods the results and less satisfactory but overall the application suggests strong potential of the proposed approach for future applications.

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Deriving global flood hazard maps of fluvial floods through a physical model cascade

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-6615-2012 ◽

2012 ◽

Vol 9 (5) ◽

pp. 6615-6647 ◽

Cited By ~ 13

Author(s):

F. Pappenberger ◽

E. Dutra ◽

F. Wetterhall ◽

H. Cloke

Keyword(s):

Land Surface ◽

Large Scale ◽

Flood Hazard ◽

Regional Scale ◽

Surface Model ◽

Return Periods ◽

Hazard Maps ◽

Data Set ◽

Flood Hazard Maps ◽

River Routing

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Design Floods Considering the Epistemic Uncertainty

Water ◽

10.3390/w13111601 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1601

Author(s):

Radu Drobot ◽

Aurelian Florentin Draghia ◽

Daniel Ciuiu ◽

Romică Trandafir

Keyword(s):

Flood Hazard ◽

Epistemic Uncertainty ◽

Statistical Distributions ◽

Hazard Maps ◽

Design Flood ◽

Flood Hydrograph ◽

Recorded Data ◽

Different Shapes ◽

Significant Spread ◽

Flood Hazard Maps

The Design Flood (DF) concept is an essential tool in designing hydraulic works, defining reservoir operation programs, and identifying reliable flood hazard maps. The purpose of this paper is to present a methodology for deriving a Design Flood hydrograph considering the epistemic uncertainty. Several appropriately identified statistical distributions allow for the acceptable approximation of the frequent values of maximum discharges or flood volumes, and display a significant spread for their medium/low Probabilities of Exceedance (PE). The referred scattering, as a consequence of epistemic uncertainty, defines an area of uncertainty for both recorded data and extrapolated values. In considering the upper and lower values of the uncertainty intervals as limits for maximum discharges and flood volumes, and by further combining them compatibly, a set of DFs as completely defined hydrographs with different shapes result for each PE. The herein proposed procedure defines both uni-modal and multi-modal DFs. Subsequently, such DFs help water managers in examining and establishing tailored approaches for a variety of input hydrographs, which might be typically generated in river basins.

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Moving to 3-D flood hazard maps for enhancing risk communication

Environmental Modelling & Software ◽

10.1016/j.envsoft.2018.11.005 ◽

2019 ◽

Vol 111 ◽

pp. 510-522 ◽

Cited By ~ 22

Author(s):

Francesco Macchione ◽

Pierfranco Costabile ◽

Carmelina Costanzo ◽

Rosa De Santis

Keyword(s):

Risk Communication ◽

Flood Hazard ◽

Hazard Maps ◽

Flood Hazard Maps

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Seaside, Oregon Tsunami Pilot Study - modernization of FEMA flood hazard maps

Open-File Report ◽

10.3133/ofr20061234 ◽

2006 ◽

Cited By ~ 4

Author(s):

Keyword(s):

Pilot Study ◽

Flood Hazard ◽

Hazard Maps ◽

Flood Hazard Maps

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Combination of geomorphic classifiers through Machine Learning-based techniques for flood hazard assessment

10.5194/egusphere-egu21-4723 ◽

2021 ◽

Author(s):

Andrea Magnini ◽

Michele Lombardi ◽

Simone Persiano ◽

Antonio Tirri ◽

Francesco Lo Conti ◽

...

Keyword(s):

Machine Learning ◽

Hazard Assessment ◽

Water Depth ◽

Flood Hazard ◽

Research Centre ◽

Hazard Mapping ◽

Hazard Maps ◽

Flood Hazard Assessment ◽

Flood Prone Areas ◽

Flood Hazard Maps

Every year flood events cause worldwide vast economic losses, as well as heavy social and environmental impacts, which have been steadily increasing for the last five decades due to the complex interaction between climate change and anthropogenic pressure (i.e.&#160;land-use and land-cover modifications). As a result, the body of literature on flood risk assessment is constantly and rapidly expanding, aiming at developing faster, computationally lighter and more efficient methods relative to the traditional and resource-intensive hydrodynamic numerical models. Recent and reliable fast-processing techniques for flood hazard assessment and mapping consider binary geomorphic classifiers retrieved from the analysis of Digital Elevation Models (DEMs). These procedures (termed herein &#8220;DEM-based methods&#8221;) produce binary maps distinguishing between floodable and non-floodable areas based on the comparison between the local value of the considered geomorphic classifier and a threshold, which in turn is calibrated against existing flood hazard maps. Previous studies have shown the reliability of DEM-based methods using a single binary classifier, they also highlighted that different classifiers are associated with different performance, depending on the geomorphological, climatic and hydrological characteristics of the study area. The present study maps flood-prone areas and predicts water depth associated with a given non-exceedance probability by combining several geomorphic classifiers and terrain features through regression trees and random forests. We focus on Northern Italy (c.a. 100000 km2, including Po, Adige, Brenta, Bacchiglione and Reno watersheds), and we consider the recently compiled MERIT (Multi-Error Removed Improved-Terrain) DEM, with 3sec-resolution (~90m at the Equator). We select the flood hazard maps provided by (i) the Italian Institute for Environmental Protection and Research (ISPRA), and (ii) the Joint Research Centre (JRC) of the European Commission as reference maps. Our findings (a) confirm the usefulness of machine learning techniques for improving univariate DEM-based flood hazard mapping, (b) enable a discussion on potential and limitations of the approach and (c) suggest promising pathways for further exploring DEM-based approaches for predicting a likely water depth distribution with flood-prone areas.&#160;

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Concept of a Polish Database of a Multi-Dimensional Cadastral System with Particular Focus on Geo-Hazards

Proccedings of 10th International Conference "Environmental Engineering" ◽

10.3846/enviro.2017.247 ◽

2017 ◽

Author(s):

Agnieszka Trystuła

Keyword(s):

Crisis Management ◽

New Technologies ◽

Flood Hazard ◽

Data Visualisation ◽

Hazard Maps ◽

Multiple Factors ◽

Dynamic Growth ◽

Cadastral System ◽

Flood Hazard Maps ◽

Cadastral Systems

The dynamic growth of contemporary cadastral systems depends on multiple factors, which include, e.g. economic policy of a given country and possibilities of implementing activities supporting innovation and transfer of new technologies. A modern cadastre should satisfy not only its leading functions, which include, e.g. fiscal, information, legal or record functions. It should also be oriented towards new challenges, including 3D geovisualisation, which will enable multidimensional visualisation of cadastral objects. New data visualisation methods will contribute to extending the existing functions of cadastral systems and to emergence of new functions, e.g. related to ensuring public safety as a basic aim of crisis management, being an important element of sustainable development. This paper presents a concept of a database of multidimensional cadastral system enabling, for instance, 3D visualisation of system objects, incorporating its known functions (e.g. fiscal, information or legal functions), and also a new purpose –support for crisis management. Additionally, the study indicates sources of data that should be used for this type of undertaking (e.g. flood hazard maps, maps of areas at risk of mass land movements, orthophotomaps).

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Global flood hazard map and exposed GDP comparison: a China case study

10.5194/nhess-2020-1 ◽

2020 ◽

Author(s):

Jerom P. M. Aerts ◽

Steffi Uhlemann-Elmer ◽

Dirk Eilander ◽

Philip J. Ward

Keyword(s):

Return Period ◽

Flood Hazard ◽

Regional Scale ◽

Flood Protection ◽

Economic Losses ◽

Return Periods ◽

Hazard Maps ◽

High Exposure ◽

Pluvial Flooding

Abstract. Floods are among the most frequent and damaging natural hazard events in the world. In 2016, economic losses from flooding amounted to $56 bn globally, of which $20 bn occurred in China (Munich Re, 2017). National or regional scale mapping of flood hazard is at present providing an inconsistent and incomplete picture of floods. Over the past decade global flood hazard models have been developed and continuously improved. There is now a significant demand for testing of the global hazard maps generated by these models in order to understand their applicability for international risk reduction strategies and for reinsurance portfolio risk assessments using catastrophe models. We expand on existing methods for comparing global hazard maps and analyse 8 global flood models (GFMs) that represent the current state of the global flood modelling community. We apply our comparison to China as a case study and, for the first time, we include industry models, pluvial flooding, and flood protection standards in the analysis. We find substantial variability between the flood hazard maps in modelled inundated area and exposed GDP across multiple return periods (ranging from 5 to 1500 years) and in expected annual exposed GDP. For example, for the 100 year return period undefended (assuming no flood protection) hazard maps the percentage of total affected GDP of China ranges between 4.4 % and 10.5 % for fluvial floods. For the majority of the GFMs we see only a small increase in inundated area or exposed GDP for high return period undefended hazard maps compared to low return periods, highlighting major limitations in the models’ resolution and their output. The inclusion of industry models which currently model flooding at higher spatial resolution, and which additionally include pluvial flooding, strongly improves the comparison and provides important new benchmarks. Pluvial flooding can increase the expected annual exposed GDP by as much as 1.3 % points. Our study strongly highlights the importance of flood defenses for a realistic risk assessment in countries like China that are characterized by high concentrations of exposure. Even an incomplete (1.74 % of area of China) but locally detailed layer of structural defenses in high exposure areas reduces the expected annual exposed GDP to fluvial and pluvial flooding from 4.1 % to 2.8 %.

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